Linepack delay measurement in fluid delivery pipeline

ABSTRACT

Technical solutions are described for predicting linepack delays. An example method includes receiving temporal sensor measurements of a first fluid-delivery pipeline network and generating a causality graph of the first fluid-delivery pipeline network. The method also includes determining a topological network of the stations based on the causality graph, where the topological network identifies a temporal delay between a pair of stations. The method also includes generating a temporal delay prediction model based on the topological network and predicting the linepack delays of a second fluid-delivery pipeline network based on the temporal delay prediction model, where a compressor station of the second fluid-delivery pipeline network compresses fluid based on the predicted linepack delays to maintain a predetermined pressure.

PRIORITY

This application is a continuation of and claims priority from U.S.patent application Ser. No. 14/976,870, filed on Dec. 21, 2015, entitled“LINEPACK DELAY MEASUREMENT IN FLUID DELIVERY PIPELINE”, the entirecontents of which are incorporated herein by reference.

BACKGROUND

The present application relates to a physical delivery system, and morespecifically, to determining topological connectivity and relativedistances from temporal sensor measurements of the physical deliverysystem.

A physical delivery system includes a pipeline system that deliversfluids, such as liquid and/or gas. For example, a gas pipeline system todeliver gas, which is used as fuel for heating, cooling, or any otherpurpose, is a physical delivery system. In the U.S. alone, there areabout 91,000 miles of gas pipelines, and 99% of the gas delivery in theU.S. is through the gas pipeline delivery system. In a physical deliverysystem, such as one that delivers natural gas, there may be delaysbetween a supply node and a delivery node, for example because naturalgas is compressible. For example, in case of natural gas, to meet anexpected demand for gas, a compressor station may pump gas ahead of theactual demand occurrence. The physical delivery system includes anetwork of pipes, that may be represented as a tree structure, with acompressor operating at a root node and trunk lines to maintain thepressure and flowrate across multiple delivery points in the treestructure.

SUMMARY

According to an embodiment, a computer implemented method for predictinglinepack delays includes receiving temporal sensor measurements of afirst fluid-delivery pipeline network, where the temporal sensormeasurements includes a series of sensor measurements from eachrespective station from the stations of the fluid-delivery pipelinenetwork. The computer implemented method also includes generating acausality graph of the first fluid-delivery pipeline network based onthe temporal sensor measurements, where the causality graph includes aset of nodes and a set of links, where the nodes are representative ofthe stations, and a pair of nodes is connected by a link in response tothe pair of stations being temporally dependent. The computerimplemented method also includes determining a topological network ofthe stations based on the causality graph, where the topological networkidentifies a temporal delay between a pair of stations in the firstfluid-delivery pipeline network. The computer implemented method alsoincludes generating a temporal delay prediction model based on thetopological network associated with the first fluid-delivery pipelinenetwork. The computer implemented method also includes predicting thelinepack delays of a second fluid-delivery pipeline network based on thetemporal delay prediction model, where a compressor station of thesecond fluid-delivery pipeline network compresses fluid beingtransported by the second fluid-delivery pipeline network based on thepredicted linepack delays to maintain at least a predetermined pressurein the second fluid-delivery pipeline network.

According to another embodiment, a system a system for predictinglinepack delays, the system includes a memory and a processor. Theprocessor receives temporal sensor measurements of a firstfluid-delivery pipeline network, where the temporal sensor measurementsincludes a series of sensor measurements from each respective stationfrom the stations of the fluid-delivery pipeline network. The processoralso generates a causality graph of the first fluid-delivery pipelinenetwork based on the temporal sensor measurements, where the causalitygraph includes a set of nodes and a set of links, where the nodes arerepresentative of the stations, and a pair of nodes is connected by alink in response to the pair of stations being temporally dependent. Theprocessor also determines a topological network of the stations based onthe causality graph, where the topological network identifies a temporaldelay between a pair of stations in the first fluid-delivery pipelinenetwork. The processor also generates a temporal delay prediction modelbased on the topological network associated with the firstfluid-delivery pipeline network. The processor also predicts thelinepack delays of a second fluid-delivery pipeline network based on thetemporal delay prediction model, where a compressor station of thesecond fluid-delivery pipeline network compresses fluid beingtransported by the second fluid-delivery pipeline network based on thepredicted linepack delays to maintain at least a predetermined pressurein the second fluid-delivery pipeline network.

According to another embodiment, a computer program product forpredicting linepack delays includes a computer readable storage medium.The computer readable storage medium includes computer executableinstructions to receive temporal sensor measurements of a firstfluid-delivery pipeline network, where the temporal sensor measurementsincludes a series of sensor measurements from each respective stationfrom the stations of the fluid-delivery pipeline network. The computerreadable storage medium also includes computer executable instructionsto generate a causality graph of the first fluid-delivery pipelinenetwork based on the temporal sensor measurements, where the causalitygraph includes a set of nodes and a set of links, where the nodes arerepresentative of the stations, and a pair of nodes is connected by alink in response to the pair of stations being temporally dependent. Thecomputer readable storage medium also includes computer executableinstructions to determine a topological network of the stations based onthe causality graph, where the topological network identifies a temporaldelay between a pair of stations in the first fluid-delivery pipelinenetwork. The computer readable storage medium also includes computerexecutable instructions to generate a temporal delay prediction modelbased on the topological network associated with the firstfluid-delivery pipeline network. The computer readable storage mediumalso includes computer executable instructions to predict the linepackdelays of a second fluid-delivery pipeline network based on the temporaldelay prediction model, where a compressor station of the secondfluid-delivery pipeline network compresses fluid being transported bythe second fluid-delivery pipeline network based on the predictedlinepack delays to maintain at least a predetermined pressure in thesecond fluid-delivery pipeline network.

BRIEF DESCRIPTION OF THE DRAWINGS

The examples described throughout the present document may be betterunderstood with reference to the following drawings and description. Thecomponents in the figures are not necessarily to scale. Moreover, in thefigures, like-referenced numerals designate corresponding partsthroughout the different views.

FIG. 1 illustrates an example fluid-delivery pipeline network, inaccordance with an embodiment.

FIG. 2 illustrates an example server, in accordance with an embodiment.

FIG. 3 illustrates a flowchart of example logic to determine topologicalnetwork of a pipeline network, in accordance with an embodiment.

FIG. 4 illustrates an example cause-effect relationship among temporalsensor measurements captured at a pair of stations, in accordance withan embodiment.

FIG. 5 illustrates a flowchart of example logic to convert a causalitygraph into a topological network, in accordance with an embodiment.

FIG. 6 illustrates an example scenario of determining a topologicalnetwork of an example pipeline network, in accordance with anembodiment.

FIG. 7 illustrates an example scenario of determining a topologicalnetwork from an example causality graph, in accordance with anembodiment.

FIG. 8 illustrates an example scenario of determining a topologicalnetwork from an example causality graph, in accordance with anembodiment.

FIG. 9 illustrates an example scenario of determining relative distancesin the stations of a pipeline network from a topological network, inaccordance with an embodiment.

FIG. 10 illustrates a system for forecasting linepack delays, inaccordance with an embodiment.

FIG. 11 illustrates a flowchart of example logic for forecastinglinepack delays, in accordance with an embodiment.

FIG. 12 illustrates a flowchart of example logic for detecting a leakcaused by a rupture in a pipeline network, in accordance with anembodiment.

FIG. 13 illustrates a flowchart of example logic for detecting a smallleak in a pipeline network that can lead to a rupture of the pipelinenetwork, in accordance with an embodiment.

FIG. 14 illustrates a flowchart of example logic for determiningthresholds for detecting a small leak in a pipeline network, inaccordance with an embodiment.

DETAILED DESCRIPTION

Establishing Topological Connectivity of a Pipeline Network

Disclosed here are technical solutions for utilizing temporal delaymeasurements of a fluid in a physical delivery system (such as, a gaspipe line system), to establish a topological connectivity of thedelivery system. Data sources, which are identified using flowmeasurements and pressure measurements, are processed (for example,using outlier removal, smoothing, and short spike removal). Theprocessed data is used to identify connectivity and temporal lagsbetween measurement nodes in the delivery system, such as by identifyingunique pairwise delays, and composing the pairwise delays into atopology tree that represents the topological connectivity. Themeasurements are used to determine the topological connectivity. Thetemporal delays that are obtained are also used to estimate a relativedistance between measurement nodes in the network.

A physical delivery system includes multiple stations interconnected bya pipeline network to deliver goods, such as fluids, from one station,such as a supply station, to one or more stations, such as destinationstations, in the delivery system. For example, the delivery system maydeliver fluids such as water, oil, compressed gas, or any other fluid.The examples throughout the present disclosure describe a deliverysystem that delivers compressed gas, however it will be obvious to aperson skilled in the art that the technical solutions are applicable toa physical delivery system irrespective of the substance that thedelivery system is transporting.

Typically, operators monitor the delivery system using a supervisorycontrol and data acquisition (SCADA) system. The SCADA system mayvisualize the physical delivery system as a network of stations and theinterconnecting pipelines, wherein the visualization is based on theoperator manually identifying the connections between the stations. TheSCADA system may be connected to one or more measurement sensors thatmeasure attributes of the transportation of the fluid in the deliverysystem. For example, the sensors may measure flow-rate, pressure,volume, or any other attribute of the flow of the fluid through thedelivery system. The sensors may be located at one or more stations.Alternatively or in addition, the sensors may be located on the pipelinenetwork, between the stations. Throughout the present disclosures, theexamples consider that the sensors are located at the stations in thedelivery stations. However, it will be obvious to a person skilled inthe art that the technical solutions are applicable to the sensorsirrespective of where the sensors are located in the delivery system.

The technical solutions described herein monitor the measurements fromthe sensors over a predetermined duration of time and automaticallyidentify the topological connectivity of the delivery system. Inaddition, the technical solutions facilitate identification of relativedistance between the sensors, and in turn the stations.

FIG. 1 illustrates an example fluid-delivery pipeline network 10. Thepipeline network 10 comprises multiple stations, each station 12connected with at least one other station 12 via a pipeline that carriesfluid between the stations. In an example, as illustrated, sensors ateach station 12 acquire measurements 15. The measurements 15 may betransmitted for reception by a server 20 over a communication network165. The server 20 may store the measurements 15 from each station 12 ina data repository 30.

The station 12 in the pipeline network 10 may be a supply station, adestination station, or a combination thereof. For example, a supplystation (such as X_(TIF)) forwards fluid received to one or more otherstations in the pipeline network 10. A destination station (such asX_(ALM)) receives the fluid via the pipelines for using the fluid,without forwarding any fluid to other stations. A combination stationmay receive the fluid, out of which a part may be used at the stationand the rest forwarded to other stations.

The station 12 may include a compressor, a fork, or any other equipmentto direct the flow of the fluid via the pipeline network 10. Inaddition, the station 12 may be equipped with sensors to acquire themeasurements 15, such as a volume, a flow-rate, a pressure, or any otherattribute of the fluid received at the station 12 and/or the fluid beingtransported from the station 12. The station 12 may further be equippedwith transmitters to transmit the measurements 15. In another example,the sensors may be equipped for the transmission of the measurements 15.

The server 20 may receive the measurements 15 and store the measurementsfrom each station 12 in the data repository 30. The server 20 may storea temporal series of sensor measurements from each station 12 thatincludes measurements 15 from each station over a predeterminedtime-span. For example, the temporal series of sensor measurement forstation X_(TIF) may include measurements 15 acquired at X_(TIF) over thepredetermined time-span, such as fifteen minutes, two hours, three days,two months, or any other time-span. The repository 30 that stores thetemporal measurements from each station 12 may be a database, a datawarehouse or any other computer readable storage accessible by theserver 20. In an example, the data repository 30 may be part of theserver 20. In another example, the data repository 30 may be at a remotelocation relative to the server 20. The server 20 and the datarepository 30 communicate over the communication network 165. Thecommunication network 165 may be a wired or a wireless communicationnetwork, or a combination of both. The communication network 165 may usea communication protocol such as transmission control protocol/internetprotocol (TCP/IP), user datagram protocol (UDP), or any other protocolor a combination thereof.

FIG. 2 illustrates an example block diagram of the server 20. The server20 may be a communication apparatus, such as a computer. For example,the server 20 may be a desktop computer, a tablet computer, a laptopcomputer, a phone, such as a smartphone, a server computer, or any otherdevice that communicates via the network 165. The server 20 includeshardware, such as electronic circuitry.

For example, the server 20 includes, among other components, a processor105, memory 110 coupled to a memory controller 115, and one or moreinput devices 145 and/or output devices 140, such as peripheral orcontrol devices, that are communicatively coupled via a local I/Ocontroller 135. These devices 140 and 145 may include, for example,battery sensors, position sensors (such as an altimeter, anaccelerometer, a global positioning satellite receiver),indicator/identification lights and the like. Input devices such as aconventional keyboard 150 and mouse 155 may be coupled to the I/Ocontroller 135. The I/O controller 135 may be, for example, one or morebuses or other wired or wireless connections, as are known in the art.The I/O controller 135 may have additional elements, which are omittedfor simplicity, such as controllers, buffers (caches), drivers,repeaters, and receivers, to enable communications.

The I/O devices 140, 145 may further include devices that communicateboth inputs and outputs, for instance disk and tape storage, a networkinterface card (MC) or modulator/demodulator (for accessing other files,devices, systems, or a network), a radio frequency (RF) or othertransceiver, a telephonic interface, a bridge, a router, and the like.

The processor 105 is a hardware device for executing hardwareinstructions or software, particularly those stored in memory 110. Theprocessor 105 may be a custom made or commercially available processor,a central processing unit (CPU), an auxiliary processor among severalprocessors associated with the server 20, a semiconductor basedmicroprocessor (in the form of a microchip or chip set), amacroprocessor, or other device for executing instructions. Theprocessor 105 includes a cache 170, which may include, but is notlimited to, an instruction cache to speed up executable instructionfetch, a data cache to speed up data fetch and store, and a translationlookaside buffer (TLB) used to speed up virtual-to-physical addresstranslation for both executable instructions and data. The cache 170 maybe organized as a hierarchy of more cache levels (L1, L2, and so on.).

The memory 110 may include one or combinations of volatile memoryelements (for example, random access memory, RAM, such as DRAM, SRAM,SDRAM) and nonvolatile memory elements (for example, ROM, erasableprogrammable read only memory (EPROM), electronically erasableprogrammable read only memory (EEPROM), programmable read only memory(PROM), tape, compact disc read only memory (CD-ROM), disk, diskette,cartridge, cassette or the like). Moreover, the memory 110 mayincorporate electronic, magnetic, optical, or other types of storagemedia. Note that the memory 110 may have a distributed architecture,where various components are situated remote from one another but may beaccessed by the processor 105.

The instructions in memory 110 may include one or more separateprograms, each of which comprises an ordered listing of executableinstructions for implementing logical functions. In the example of FIG.2, the instructions in the memory 110 include a suitable operatingsystem (OS) 111. The operating system 111 essentially may control theexecution of other computer programs and provides scheduling,input-output control, file and data management, memory management, andcommunication control and related services.

Additional data, including, for example, instructions for the processor105 or other retrievable information, may be stored in storage 120,which may be a storage device such as a hard disk drive or solid statedrive. The stored instructions in memory 110 or in storage 120 mayinclude those enabling the processor to execute one or more aspects ofthe systems and methods of this disclosure.

The server 20 may further include a display controller 125 coupled to auser interface or display 130. In some embodiments, the display 130 maybe an LCD screen. In other embodiments, the display 130 may include aplurality of LED status lights. In some embodiments, the server 20 mayfurther include a network interface 160 for coupling to a network 165.The network 165 may be an IP-based network for communication between theserver 20 and an external server, client and the like via a broadbandconnection. In an embodiment, the network 165 may be a satellitenetwork. The network 165 transmits and receives data between the server20 and external systems. In some embodiments, the network 165 may be amanaged IP network administered by a service provider. The network 165may be implemented in a wireless fashion, for example, using wirelessprotocols and technologies, such as WiFi, WiMax, satellite, or anyother. The network 165 may also be a packet-switched network such as alocal area network, wide area network, metropolitan area network, theInternet, or other similar type of network environment. The network 165may be a fixed wireless network, a wireless local area network (LAN), awireless wide area network (WAN) a personal area network (PAN), avirtual private network (VPN), intranet or other suitable network systemand may include equipment for receiving and transmitting signals.

The server 20 may be part of a supervisory control and data acquisition(SCADA) system. Typically, there is no topological relationship in theSCADA system; therefore, the technical solutions facilitate the SCADAsystem to obtain, in an automated manner, a physical/topological networkof the pipeline network 10 by mapping of the measurements 15 between thestations of the pipeline network 10. The technical solutions determinethe topological network based on the measured physical properties of thefluid being delivered, such as, pressure, flow rate, by identifying atime delayed effect from one station in the pipeline network 10 to aconnected station in the pipeline network 10. In an example, the delayedeffects may be statistically evaluated to automate the process ofmapping the measurements 15 to the topological network.

FIG. 3 illustrates a flowchart for the server 20 to determine thetopological network of the pipeline network 10. The server 20 receivesor accesses the temporal sensor measurements of the stations of thefluid-delivery pipeline network, as shown at block 305. For example, theserver 20 receives the measurements 15 from the sensors at the stations,or alternatively accesses the measurements 15 from the data repository30. The server 20 cleans the data in the measurements 15 in preparationof determining the topological network of the pipeline network 10, asshown at block 310. For example, the cleaning may include removal ofoutliers in each temporal series of respective stations, as shown atblock 312. In addition or alternatively, the server 20 may smooth eachtemporal series of sensor measurements, as shown at block 314. Inaddition or alternatively, the server 20 may remove short spikes fromeach series of sensor measurements, as shown at block 316. In additionor alternatively, the server 20 may perform other data cleaningoperations on the temporal sensor measurements in other examples.

The server 20 further analyzes the temporal sensor measurements todetermine causality between temporal sensor measurements of the stationsin the pipeline network 10, as shown at block 320. For example, theserver 20 analyzes the temporal sensor measurements of the stations in apairwise manner, to identify if measurements 15 observed at a firststation affect the measurements 15 at a second station. For example, asillustrated in FIG. 4, if a compressor operation at an upstream station,such as X_(POG) (see FIG. 1), may have an effect at a downstreamstation, such as X_(DN). As illustrated in FIG. 4, the effect at thedownstream station may be observed after a time delay, for example dueto the time taken by the fluid to flow to the downstream station.

For example, the server 20 determines a causality model using themultiple series of measurement data from each of the stations, as shownat block 322. For example, the causality model may be determined basedon multivariate regression, such as using Granger model, in which givenP number of time series, X₁-X_(P), the model may determine each timeseries X_(i) that represents the causes. Table 1 illustrates an examplecausality model.

TABLE 1${X_{i}(t)} = {{\sum\limits_{j = 1}^{P}{\alpha_{i,j}^{T}X_{j}^{t,{Lagged}}}} + \epsilon}$where: X_(j) ^(t,Lagged) = [X_(j)(t − L), . . . , X_(j)(t − 1)] is thelagged time series α_(i,j) ^(T) = [α_(i,j,1), . . . , α_(i,j,L)] is thecoefficient vector. If any of the α_(i,j,) {1, . . . , L} ≠ 0 

 X_(j) → X_(i) possible challenges: for high dimensional data, when L islarge, we have p × L number of features in the regression, it ispossible that the model picks up smalle causal effects. Therefore, it isimportant to have some penalization so that the casual relationship ismost significant.

Thus, the server 20 computes a plurality of temporal lags X_(i)(t) andcorresponding coefficients α_(i), such as using multivariate regressionanalysis using L1 penalty. The server identifies the temporal lagX_(k)(t) that has the maximum corresponding coefficient α_(k). Theidentified temporal lag X_(k)(t) is then used in subsequent computationsand identification of the temporal connectivity of the pipeline network.

The server 20 further identifies pairwise connectivity of the stationsbased on the causality, and generates a causality graph, as shown atblocks 330. For example, the server identifies a penalization model tofilter the causality relationships identified, as shown at block 332.For example, the penalization model may be based on one of severaltechniques such as a Grouped-Lasso-Granger, a Lasso regression, or aGrouped-Lasso regression, among others. Table 2 illustrates models ofexample penalization models.

TABLE 2 Grouped-Lasso-Granger Grouped-Lasso (

₁) penalty is used to obtain a sparse graph structure:${\min\limits_{\{\beta\}}{\sum\limits_{t = {L + 1}}^{T}{\sum\limits_{i = 1}^{P}{{{X_{i}(t)} - {\sum\limits_{i = 1}^{P}{\beta_{i,j}^{T}X_{j}^{t,{Lagged}}}}}}_{2}^{2}}}} + {\lambda{\sum\limits_{i = 1}^{P}{\beta_{i}}_{1}}}$Lasso regression uses

₁ penalty, which tend to “push” coefficients to zero, therefore, arrivesat a sparse structure, capturing the most important temporal dependencybetween time series. Grouped-Lasso regression penalize the sum of thecoefficients of lagged series from one time series, therefore willarrive at a sparse structure in pair-wise casual graph. In other words,we can reduce the number of non-zero coefficients on the casual effectfrom one time series to effect time series, thus reduce multipletemporal casuality problem.

Based on the causality model and the penalization model, the server 20generates a causality graph of the temporal sensor measurements. Thecausality graph includes a set of nodes and a set of links. The nodesare representative of the stations of the pipeline network 10. A pair ofnodes in the causality graph is connected by a link in response to thecorresponding pair of stations being temporally dependent. The causalitygraph is non-cyclical.

Referring back to FIG. 3, the server determines a topological network ofthe pipeline network 10 based on the causality graph, as shown at block340. FIG. 5 illustrates a flowchart of determining the topologicalnetwork of the pipeline network 10 based on the causality graph. Theserver 20 traverses the causality graph recursively to identify nodeswith at least one-level subnetworks, as shown at block 505. A node withat least one-level subnetwork is a node that is connected to at leastone other node that corresponds to a downstream station. The server 20further selects a node from the nodes with at least one-levelsubnetworks and identifies the first-level connections of that node, asshown at block 510. The first level connections are direct connections.For example, in FIG. 6, in the illustrated causality graph 620, X₂ is afirst-level connection of the node X₃, while X₁ is a second-levelconnection of X₃. Referring back to FIG. 5, consider that the selectednode is N_0 with a set of first-level connections {N_1, N_2, . . . N_q}.The set of first-level connections is identified by starting at N_0 andtraversing the causality graph to identify a node that is directlylinked with N_0, as shown at block 512. Once a node is identified asbeing directly linked with N_0, the server 20 adds the node to the setof first-level connections corresponding to N_0. The server 20 ranks thenodes in the set of first-level connections according to temporal lagsof the nodes, as shown at block 514. Thus, the set of first-levelconnections {N_1, N_2, . . . N_q} includes nodes that are orderedaccording to the temporal lags. In an example, the ordering may be in anincreasing manner, thus N_1 has least temporal lag in the ordered set.In another example, the ordering may be in decreasing manner, in whichcase N_q has the least temporal lag. The server filters the set offirst-level connections {N_1, N_2, . . . N_q} by removing nodes in theset that have a direct connection with the node with the least temporallag, as shown at block 516. For example, for all the nodes N_i, where iis from 2 to q, the server deletes N_i from the ordered set {N_1, N_2, .. . N_q}, if there is a connection between N_1 and N_(i−1).

The server 20 further recursively repeats the process for all the nodesin the set of first-level connections {N_1, N_2, . . . N_q}, as shown atblock 520. The server 20 ensures that all the nodes in the causalitygraph are analyzed in this manner, as shown at block 530. The resultingfirst-level connection sets for each respective node in the causalitygraph is the temporal connectivity of the pipeline network 10 accordingto pairwise causality among the stations in the pipeline network 10.

Referring back to FIG. 3, the server 20 determines relative distancesbetween the stations of the pipeline network 10 based on the topologicalnetwork, as shown at block 350. For example, the relative distances maybe the temporal lags of the nodes in the topological network. In anexample, the relative distance of a node may be a scaled value based onthe temporal lag of that node and a predetermined scaling value. Theserver 20 further communicates the topological network and/or therelative distances for display, as shown at block 360.

FIG. 6 illustrates an example scenario of determining the topologicalnetwork. In the example scenario, consider a pipeline network with fourstations—X₁, X₂, X₃, and X₄. The server 20 generates an optimizedcausality model 610 based on the temporal sensor measurements capturedat each station. The server 20 identifies pairs of stations that have acause-effect relationship according to the temporal sensor measurements.The server 20 accordingly populates a causality graph 620. The server 20associates a temporal lag with each link in the causality graph 620. Thetemporal lag represents a time delay for a change in temporal sensormeasurements at a first node to reflect in the temporal sensormeasurements at a second node. The server 20 further determines atopological network 630 of the exemplary pipeline network of the fourstations.

FIG. 7 illustrates determining the topological network 630 from thecausality graph 620 from the example scenario of FIG. 6 according to theflowchart illustrated in FIG. 5. For example, the server selects node X₄as the starting node N_0, as shown at block 510. In an example, the nodeX₄ is selected because the node is sequentially the first supply node ofthe pipeline network. The set of first-level connections for X₄ includesthe nodes X₁ and X₂, as shown at 710. The server 20 keeps track of thetemporal lag associated with the nodes identified. The server 20 filtersthe first level connections for X₄ 710 to generate a filtered firstlevel connections for X₄ 720. The filtered first level connections forX₄ 720 does not include the node X₂, which the server filters since X₂is directly connected to X₁, which is the node with the least temporallag in the first level connections for X₄ 710. The server 20 repeats theprocess recursively for all the first level connections 710 to find nextlevel connections, such as shown at 730, until all nodes are analyzedand the resulting topological network 630 is obtained and displayed.

FIG. 8 illustrates determining a topological network 830 from acausality graph 805 based on the example scenario of FIG. 6 according tothe flowchart illustrated in FIG. 5. A difference between the causalitygraph 805 in this second example from the causality graph 620 of theprevious example is that the nodes X₁ and X₂ are not directly connectedin this example. Again, the server 20 selects the node X₄ as thestarting node N_0 and identifies a set of first-level connections for X₄810 that includes the nodes X₁ and X₂. The server 20 further filters theset to determine a set of filtered first level connections for X₄ 820.As indicated earlier, in this example, the filtering does not remove anynode from the set of first-level connections for X₄ 810. Accordingly,after recursively repeating the process for each node in the causalitygraph 805, the server 20 determines the topological network 830.

FIG. 9 illustrates determining relative distances between the nodes inthe topological network. The temporal lag associated with the links ofthe nodes added to the set of first-level connections may be used todetermine the relative distance. As illustrated in the example of FIG.9, the temporal lags are used as the relative distance itself.Alternatively, the temporal lags may be scaled by a predetermined valueto obtain the relative distances.

Thus, the technical solutions describe determining a causality model,such as based on Granger/Lasso models, for identifying pairwise delaysin stations in a fluid-delivery pipeline network, and using thecausality model to convert the pairwise delays into a topology tree ofthe pipeline network. Further, based on the pairwise delays, relativedistances between the stations may be determined.

Forecasting Linepack Delay in a Pipeline Network

The technical solutions described herein further facilitate determiningthe temporal delays between the stations in the pipeline network 10, andfurther estimating a linepack delay in the pipeline network 10 based onthe identified temporal delays. Linepack is the amount of fluidmaintained in the pipeline network 10 to maintain a predeterminedpressure in the pipeline network 10. The technical solutions facilitateestimating the linepack delays in non-monitored pipeline networks, orportions thereof. Such forecasts facilitate a compressor station to pumpfluid, such as gas ahead of the a demand occurrence at a deliverystation, thus overcoming a time delay between supply and deliverystations in case of compressible fluid, such as natural gas.Accordingly, the technical solutions facilitate improving an operationof a pipeline network, such as a high-pressure gas transmission systemto meet the demand at delivery stations in the transmission system.

In the high-pressure gas transmission system, it is important for anoperator to have information on the temporal delay to scale changes oflinepacks at one point to another connected point for planning purposes.The technical solutions described herein facilitate computing thelinepack cascading effect on connected nodes in the gas transmissionsystem. Further, the technical solutions facilitate computing thelinepack cascading effect in a gas transmission system that may not beequipped with a SCADA system. In an example, the technical solutionsinclude computing delayed effects in a SCADA equipped gas transmissionsystem and using machine-learning techniques to determine a modelbetween a geo-spatial distance, flow-rate, and pressure with linepacktemporal delay effect. The model is subsequently used to predict thelinepack effect on a gas transmission system that is not SCADAmonitored, and further used to forecast the linepack effect on cascadingsystems. In another example, the model is used to predict the linepackeffect on the gas transmission that is configured in a different way.For example, the model is used to predict an effect on the linepackdelay if pressure, temperature, frequency of rotations, or any otherparameter at a specific station in the gas transmission is changed.

FIG. 10 illustrates a system to forecast linepack delays in anon-monitored pipeline network. The system includes. Among othercomponents, a forecasting server 1030 and two fluid-delivery pipelinenetworks, a monitored pipeline network 1010 and a non-monitored pipelinenetwork 1020. The monitored pipeline network 1010, which may be similarto the pipeline network 10 described herein, includes the measurementsensors and the server 20, which may be part of a SCADA system thatmonitors the measurements 15 of pipeline network 1010. The non-monitoredpipeline network 1020 is similar to the pipeline network 10 or themonitored pipeline network 1010 in other respects except that thenon-monitored pipeline network 1020 does not include sensors and/or aserver to monitor measurements at stations of the non-monitored pipelinenetwork 1020. In FIG. 10, the monitored pipeline network 1010 and thenon-monitored pipeline network 1020 are illustrated to differentstructure of the respective stations. However, in an example, the twopipeline networks may have same structure of the stations. In anotherexample, the non-monitored pipeline network 1020 may be a portion of themonitored pipeline network 1010, where the non-monitored pipelinenetwork 1020 is the portion that does not include measurement sensors orwhich is not yet connected to communicate with the server 20.Alternatively or in addition, the non-monitored pipeline network 1020may be a new pipeline network that does not have measurements 15captured for at least threshold duration.

The amount of gas being transferred by the monitored pipeline 1010 andthe non-monitored pipeline network 1020 may be the same, in an example.In another example, the monitored pipeline 1010 and the non-monitoredpipeline network 1020 may transport different amounts of gas. In yetanother example, the monitored pipeline 1010 and the non-monitoredpipeline 1020 are identical in physical dimensions, such as pipelinediameters, lengths, elevation, and other aspects. The non-monitoredpipeline 1020 may differ from the monitored pipeline 1010 in one or moreof the other parameters, such as the compression at the compressorstations, amount of gas being transferred, compression ratio of thefluid transferred, the frequency of rotations at the compressorstations, flow-rate at the stations, or any other such measurement. Theserver 20, based on the model generated using the monitored pipelinenetwork 1010, predicts the linepack delays in the non-monitored pipeline1020, with identical physical dimensions, but different parameters.

The forecasting server 1030 is a server computer similar to the server20 and includes at least the components of the server 20 as illustratedin FIG. 2. The forecasting server 1030 may access the data repository 30that includes the temporal sensor measurements from the stations of themonitored pipeline network 1010. In an example, the forecasting server1030 and the server 20 may be one and the same.

FIG. 11 illustrates a flowchart for forecasting the linepack delay inthe non-monitored pipeline network 1020. The forecasting server 1030determines a topological network of the monitored pipeline network 1010based on a causality graph for the stations in the monitored pipelinenetwork 1010, as shown at block 1105. In another example, theforecasting server 1030 accesses the topological network of themonitored pipeline network 1010 from the data repository 30, where theserver 20 determines and stores the topological network in the datarepository 30. The forecasting server 1030 further determines (oraccesses) the temporal delays (or lags) between each connected pair ofstations from the topological network of the monitored pipeline network1010, as shown at block 1110. The topological network and the temporallogs of the monitored pipeline network 1010 may be determined asdescribed herein, such as according to the flowchart illustrated in FIG.3.

The forecasting server 1030 generates a temporal delay prediction modelbased on the topological network and the relative distances of themonitored pipeline network 1010, as shown at block 1120. In an example,the forecasting server 1030 uses topological network and relativedistances from more than one monitored pipeline network to generate thetemporal delay prediction model. Generating the temporal delayprediction model may be based on machine learning techniques such asgradient boosting machine or any other machine learning technique. In anexample, the forecasting server 1030, for each connected pair of nodesin the topological network of the monitored pipeline network 1010,identifies values of a set of predetermined attributes 1112, as shown atblock 1122. For example, the predetermined attributes 1112 may include alength of the pipeline between the pair of nodes, a diameter of thepipeline, an elevation difference of the pipeline between the pair ofnodes, or the like. The forecasting server 1030 may further compute aset of predetermined values 1114 based on the temporal sensormeasurements of each connected pair of nodes in the topological networkof the monitored pipeline network 1010, as shown at block 1124. Thepredetermined values 1114 may include an average operating pressure, anaverage operating flow rate, or the like or a combination thereof.

The forecasting server 1030 creates a mapping between temporal delaysbetween a pair of connected stations of the topological network of themonitored pipeline network 1010 and the corresponding values of thepredetermined attributes 1112 and predetermined measurements 1114, asshown at block 1126. For example, the forecasting server 1030 maps thegeo-spatial distance, average flow rate, and/or average pressure withthe temporal delay between a pair of connected stations of thetopological network of the monitored pipeline network 1010. For example,the forecasting server 1030 maps the temporal delay between a pair ofstations of the monitored pipeline network 1010 to the correspondingvalues of the predetermined attributes 1112 and measurements 1114. Theforecasting server 1030, based on multiple such mappings for multiplepairs of stations in the monitored pipeline network 1010, identifies arelationship between the temporal delays and the correspondingpredetermined attributes and measurement values.

The forecasting server 1030 accesses attributes of the non-monitoredpipeline network 1020, as shown at block 1130. In an example, theforecasting server 1030 receives the attributes of the non-monitoredpipeline network 1020 via a communication network. The attributes of thenon-monitored pipeline network 1020 includes attributes of the stationsof the non-monitored pipeline network 1020. The attributes include oneor more of the predetermined attributes 1112, such as a length of thepipeline between the pair of nodes, a diameter of the pipeline, anelevation difference of the pipeline between the pair of nodes, or thelike.

The forecasting server 1030 forecasts linepack delays at the stations ofthe non-monitored pipeline network 1020 based on the temporal delayprediction model, as shown at block 1135. For example, for a first pairof stations in the non-monitored pipeline network 1020, the forecastingserver 1030 identifies a second pair of stations from the monitoredpipeline network 1010 with matching attributes 1112. Accordingly, theforecasting server 1030 maps the temporal delays of the second pair ofstations with the first pair, and forecasts temporal delays similar oridentical to those associated with the first pair of stations. Inanother example, the forecasting server 1030 uses the relationshipsidentified between the temporal delays and the attributes 1112 and themeasurements 1114, from the monitored pipeline network 1010, to forecastthe temporal delays in the non-monitored pipeline network 1020.

In a pipeline network that is transporting a highly compressible fluidsuch as natural gas, the temporal delays are representative of thelinepack delays in the high-pressure gas transmission system. A‘linepack’ is an amount of gas in the high pressure gas transmissionsystem, for example, the amount of gas maintained in the gastransmission system at all times to maintain pressure and effectuninterrupted flow of gas through the pipeline network 10. To meet anexpected demand for gas, a compressor station pumps gas ahead of thedemand occurrence. The forecasting server 1030, by forecasting linepackdelays, facilitates the compressor station to meet the expected demandwhile maintaining the linepack according to compliance values.

Accordingly, the technical solutions described herein facilitate usingmachine learning techniques, such as gradient boosting, to determine amapping function between a set of features, such as geo-spatialdistance, average flow rate, average pressure, with temporal delay ofthe linepacks in the high pressure gas transmission system. The mappingfunction is determined based on temporal sensor measurements from amonitored pipeline network. The technical solutions further facilitateapplying the mapping function to predict linepack delay effects on apipeline network that is not being SCADA monitored.

Forecasting Leaks Caused by Ruptures in Pipeline Network

The pipeline network 10 may be part of a complex system such as a highpressure gas pipeline system, which transports fluid that is unsteady,and has a compressible flow, with frequent compressor operations. Insuch a pipeline network, the flow is not unidirectional as a compressorstation may serve multiple close-by locations. Additionally oralternatively, the flow direction of the fluid may change due to ascheduled operation, pigging, or an unanticipated leak event. In anexample, the nature of compressibility of the fluid (such as gas) andfluid dynamics, there is a temporal lag for any change at one locationto take effect on connected locations at different speed under differentoperation conditions. The temporal lag may lead to measurement errors.For example, flow measurements may show high frequency oscillationsand/or short spikes. The accuracy of the flow measurements may varybased on the magnitude of the flow. Since accurate flow measurement isdifficult, using principles such as of “mass conservation” to detect aleak in the pipeline network 10, such as transporting high-pressure gas.In addition, some measurements may not be available from each station inthe pipeline network 10. Further yet, the flow in the pipeline network10 may be unpredictable due to sudden, unplanned demands such as fromcommercial loads like gas turbines in electric plants, potato plants, orthe like. Additionally or alternatively, the flow in the pipelinenetwork 10 may be unpredictable due to weather conditions, such asstorms, hurricanes, floods, and the like. Accordingly, accuratelypredicting a flow pattern in the pipeline network 10 is difficult.

The technical solutions described herein facilitate leak detection forthe pipeline network 10, such as a high-pressure gas transmissionnetwork. Although the examples described herein use a high-pressure gastransmission network, it will be obvious to a person skilled in the artthat the technical solutions embodied by the examples are applicable toany other pipeline network. The technical solutions determinetopological connectivity and relative distance from temporal sensormeasurements of a high-pressure gas transmission system; estimatetemporal delays of linepacks between connected delivery points in thehigh-pressure gas transmission system; and forecast leaks in thehigh-pressure gas transmission system. The leaks may be caused, forexample, by a rupture of the gas pipeline infrastructure. In anotherexample, the leaks may be caused by small damage to the pipelinenetwork, which over an extended period of time, such as days, weeks,months, or any other period of time, leads to a rupture event. Thetechnical solutions forecast the leaks based on identification ofpatterns in the temporal sensor measurements captured at the stations inthe high-pressure gas transmission system. Accordingly, the technicalsolutions facilitate a real-time leak detection, to prevent rupture leakevents in the future.

Typically, a SCADA uses a single point of reference to determine a leakin the pipeline network 10. The technical solutions described,facilitate an improved technique for forecasting a leak (and thus, inturn a rupture) by taking into account an interaction between differencemeasurements, and from different stations.

FIG. 12 illustrates a flowchart for forecasting a leak in the pipelinenetwork 10 based on the temporal sensor measurements. In an example, theserver 20 implements the method represented by the flowchart. The server20 determines the topological network of the pipeline network 10, asshown at bock 1205. For example, the server 20 determines thetopological network as described herein (see FIG. 3, for example). Theserver 20 further identifies a subsystem in the topological network, asshown at block 1210. A subsystem may be a subsection of the pipelinenetwork 10, which includes, for example, topologically connectedstations that are within a predetermined distance from each other. In anexample, the server identifies a subsystem that includes two stationsthat are directly connected in the topological network. In anotherexample, the server 20 identifies a subsystem that includes a compressorstation and the delivery stations receiving compressed fluid from thecompressor station.

The server 20 accesses the historical temporal sensor measurements forthe stations in the subsystem and synchronizes the temporal sensormeasurements across the subsystem, as shown at block 1220. For example,the server 20 accesses the historical temporal sensor measurements, suchas pressure, flow-rate, volume, or any other sensor measurements for thestations from the data repository 30. The historical temporal sensormeasurements for a predetermined time-span may be accessed, for example,last six months, last one year, last 30 days, or any other predeterminedtime-span. In an example, the predetermined time-span is selected suchthat the pipeline network 10 has experienced one or more leaks due toruptures of the infrastructure within the selected time-span.

In an example, the server 20 identifies the temporal lags between thetemporal sensor measurements of the stations of the subsystem, as shownat block 1222. For example, the server 20 may compute the temporal lagsbetween two stations by finding the max correlation between the two timeseries values of the temporal sensor measurements at the two stations.For example, the computation may be expressed as,

$\rho_{X,Y} = {{{corr}\left( {X,Y} \right)} = {\frac{{cov}\left( {X,Y} \right)}{\sigma_{X}\sigma_{Y}} = \frac{E\left\lbrack {\left( {X - \mu_{X}} \right)\left( {Y - \mu_{Y}} \right)} \right\rbrack}{\sigma_{X}\sigma_{Y}}}}$

In an example, the temporal lags are computed for each model training.

The server 20 generates a prediction model for each of the stations inthe subsystem based on the historical temporal sensor measurements. Theprediction model for each station may be unique. In another example, theserver 20 generates the prediction models for each station in parallel.For example, the server 20 generates a prediction model for a firststation in the subsystem, as shown at block 1230. The prediction modelpredicts sensor measurements at the first station based on the sensormeasurements at each station in the subsystem. Thus, the predictionmodel determines a relationship between the temporal sensor measurementsof the stations in the subsystem, after synchronizing the temporalsensor measurements. For example, if the sensor measurement predicted ispressure, the prediction model determines a multivariate relationship,which may be expressed as in Table 3.

TABLE 3 P₀ ~ 

 (f₀, . . . , f_(n), P₁, . . . , P_(n), T, C₀, . . . , C_(n)) (5) where:n is the number of stations, and measurement points in the subsystem. P₀is the control variable, i.e., pressure measurements in the subsystem,f₀, . . . , f_(n) are the upstream/downstream flows in the pipe segmentf₀ is the flow measurement from the same station as P₀ P₁, . . . , P_(n)are the pressure measurements at up-stream and down- stream of the pipesegment, T is the air temperature C₀, . . . , C_(n) are the compressorRPMs.

The relationship F, may be a linear model, which captures conditionaldistribution of the control variables and the other variables in thesubsystem. In another example, the relationship may be non-linear. Theserver 20 may compensate the measurements from the stations according tothe temporal lags between the stations. The server 20 determines therelationship by using machine learning techniques. For example, theserver 20 may use a neural network to iterate over the historicaltemporal sensor measurements from the stations in the subsystem fromdifferent time-spans. For example, the server 20 may divide thehistorical temporal sensor measurements into multiple segments accordingto predetermined time-spans, such as one week, two weeks, ten days, orany other time-segments. The server 20 may determine relationships F₁,F₂ . . . F_(n) for the first station for each time-segment, where theserver 20 divides the historical temporal sensor measurements into nsegments. The server 20 determines the relationship F for the firststation based on the multiple relationships. The server 20 may use othermachine learning techniques to determine the relationship F in otherexamples. The server 20 determines the relationship for each station inthe subsystem in this manner.

The server 20 further computes sensor measurements at the stations inthe subsystem based on the historical sensor measurements according tothe prediction model for the respective station. For example, the server20 computes predicted sensor measurements for the first station,according to the relationship model F, using the historical temporalsensor measurements of the rest of the station in the subsystem, asshown at block 1240. Thus, the server 20 computes the predicted sensormeasurements using the same historical temporal sensor measurements thatwere used to determine the prediction model. The server 20 compares thepredicted sensor measurements with the actual temporal sensormeasurements at the first station and computes deviations between thetwo, as shown at block 1252. For example, the server 20 determines rootmean square error (RMSE) between the predicted sensor measurements andthe actual sensor measurements at the first station. In an example, thesensor measurements may be pressure, flow-rate, volume, or any othersensor measurements. The server 20, in an example, may plot thedeviations over time-span of the historical measurements. The server 20may statistically analyze the deviation values, such as by fitting aGaussian distribution, computing a mean, and/or a standard deviation ofthe deviation values, as shown at block 1254.

The server 20 may identify a threshold deviation from the deviations,such as by computing mean, standard deviation, or a coefficient ofvariation of the deviation values. In another example, the server 20 maymap the deviations with the timestamps corresponding to the historicaltemporal sensor measurements, as shown at block 1256. For example, ifthe historical temporal sensor measurements include measurementscaptured every day for a month, the timestamps may represent each day ofthe month. In such a case, the server 20 computes the deviationscorresponding to each day of the month for which the historical sensormeasurements are acquired. Further, server 20 may identify thedeviations corresponding to timestamps at which known leak events hadoccurred. The server 20 may determine a second level deviation, that isa deviation in the deviations, at which the leak events occurred andidentify the threshold deviation based on the deviations correspondingto the known leak events.

Further yet, the server 20 may use the second level deviations to reducefalse alarms, as shown at block 1256. For example, the thresholddeviation that is identified based on statistical analysis of thedeviations, such as by computing a mean, or coefficient of variance ofthe deviation values is fine-tuned by mapping the deviation values withthe known leak events. For example, the threshold deviation value iscompared with the actual deviation values corresponding to the leakevents and the threshold deviation modified to be closer to the actualdeviation value, if the threshold deviation would have missed the actualdeviation value. For example, if the actual deviation value is smallerthan the threshold deviation value, the threshold deviation value may bemodified to the actual deviation value. In other words, a minimum (ormaximum) of the actual deviation values corresponding to the leak eventsand the threshold deviation may be used to replace the thresholddeviation. Thus, the server 20 finds a threshold deviation value(multiples of standard deviation), that identifies the significant leakevents and also minimize false alarms at the same time, as shown atblock 1250.

The server 20 may communicate the threshold deviation to the SCADA sothat the sensor measurements at the first station may be monitored andif the sensor measurements above (or below) the threshold value isdetected, an indication of a leak in the subsystem may be triggered. Forexample, the indication may be triggered by sending a message to anadministrator or other employee monitoring the pipeline network 10.Alternatively, fluid transmission in the subsystem may be stoppedtemporarily while the leak is detected and repaired. Of course, otheractions may be taken in response to the prediction of the leak even bythe server 20. Thus, the server 20 facilitates forecasting a leak in thesubsystem of the pipeline network 10 based on the temporal sensormeasurements of the first station by determining threshold deviationvalues for the sensor measurements at the first station. In addition,the server 20 facilitates forecasting the leak in the subsystem based ontemporal sensor measurements at any of the stations in the subsystem.Further, by forecasting leaks in all the subsystems in the pipelinenetwork 10, the server 20 may predict leaks across the entire pipelinenetwork 10 that are caused by a rupture of the pipeline infrastructure.

In an example, the server 20 uses the pressure measurements at thestations to predict leak events that are caused by ruptures. In anotherexample, the server 20 predicts the pressure measurements at thestations based on pressure measurements, flow-rate measurements, volumemeasurements, and other measurements at the other stations in thesubsystem. In addition, the server 20 may use air-temperaturemeasurements, a number of stations in the subsystem, and a number ofrotations, and a frequency of rotations (such as RPM) at compressorstations in the subsystem. The prediction model may further dependdifferently on measurements from upstream and downstream stations. Afirst station is referred to as an upstream station in relation to asecond station, if the first station supplies fluid to the secondstation. Alternatively, if the second station supplies fluid to thefirst station, the first station is referred to as a downstream stationin relation to the second station. The prediction model may furtherdepend on compressor energy usage, or any measurement that indicates theoperating condition of the compressor or any other station in thepipeline. The above listed measurements and other measurements listedthroughout the present disclosure are exemplary, and it will be obviousto a person skilled in the art that other measurements may be used asreplacements, alternatives, or in addition in the embodiments of thetechnical solutions described throughout the present disclosure.

In another example, the server 20 uses a combination of sensormeasurements at the stations to predict/detect leak events caused byrupture. For example, the server 20 determines a pressure thresholdvalue for the first station as described herein. Further, the server 20determines a flow-rate threshold value for the first station in asdescribed herein. The server 20 communicates the two threshold values tothe SCADA. Alternatively, the server 20 is part of the SCADA andmonitors the sensor measurements across the pipeline network 10. Theserver 20 may detect that pressure at the first station is lower thanthe pressure threshold learned from the historical temporal sensormeasurements, and a flow-rate is higher than the flow-rate threshold.The server 20 concludes that the lower pressure and high spike in flowindicates that there are leaks happening in the subsystem, and triggersan indication accordingly. Of course, in other examples, othercombination of sensor measurements may be used.

Thus, the technical solutions described herein identify subsystems andthe temporal measurements of physical properties, such as flow orpressure measurements. The measurement data may be processed such as byperforming outlier removal, smoothing, short spike removal, or othersuch operations. The technical solutions further facilitate developing asynchronization model for synchronizing temporal measurements acrossdifferent stations according to temporal lags. The technical solutionsfurther facilitate determining a prediction model to determine arelationship between sensor measurements at a station and sensormeasurements at other stations in the subsystem. The prediction modelmay be used to identify thresholds and anomalies based on known normaland leak events that occurred corresponding to the historical sensormeasurements used to generate the prediction model. The technicalsolutions further include monitoring the pipeline network todetect/predict leak in the subsystem in response to detecting sensormeasurements below and/or above determined threshold values. Thetechnical solutions further include identifying anomaly scores toprevent false alarms by mapping determinations with historical sensormeasurements and known leak events.

The technical solutions thus facilitate detecting leaks that are causedby a rupture in the pipeline network by using sensor measurements frommultiple points of reference, and by taking into account the interactionbetween difference measurements, from different stations. By consideringa single point of references, small anomalies in the system may bemissed, leading to inaccuracies. Thus, the technical solutions overcomesuch inaccuracies in addition to the advantages that are describedherein.

Detecting Signature Pattern of Small Leaks Caused by Small Damage on thePipeline Network

The technical solutions described herein further include using thetemporal delay model and the prediction delay model of the temporalsensor measurements in the pipeline network, such as the high pressuregas pipeline system, to identify a benchmark values that representoperation of the pipeline network without leaks. Based on the benchmarkvalues, the technical solutions further facilitate continuouslydetecting anomalies in the sensor measurements at the stations in thepipeline. The anomalies identified may be caused by relatively smalldamages to the pipeline that may lead to extended small leaks or evenruptures of the pipeline.

For example, the small damage may be a minor crack (compared to arupture), or a partial clog, or any other damage that has not yet causeda large-scale problem in the pipeline network. The small damage causessmall changes on the sensor measurement patterns such that systemoperation experts cannot detect the damage using SCADA monitoringsystem. An extended small leak may be a leak that leaks fluid below apredetermined threshold flow-rate and for at least a predeterminedduration. For example, the server 20 may determine that leaks below thepredetermined threshold flow-rate such as 1 milliliter per second, 5milliliter per second, 5 liters per second, or any other flow-rate, is a‘small leak.’ Of course, other examples may have other flow-rates thatare considered small. An extended leak may be a leak that goesunrepaired (or unnoticed) for at least a predetermined time duration,such as 5 days, 10 days, or any other time duration. Accordingly, anextended small leak is a small leak that may continue unrepaired (orunnoticed) for at least the predetermined duration. The extended smallleaks may cause relatively larger problems, such as erosion, rupturesand so on in the pipeline network. The technical solutions describedherein facilitate detecting leaks in a pipeline network that are notdetectable by the SCADA system over an extended period of time, such asdays, weeks, months, or any other period of time.

FIG. 13 illustrates a flowchart for detecting signature pattern of smallleaks caused by small damage on the pipeline network. The server 20 mayimplement the method illustrated by the FIG. 13, in an example.

The server 20 determines the topological network of the pipeline network10, as shown at bock 1305. For example, the server 20 determines thetopological network as described herein (see FIG. 3, for example). Theserver 20 further identifies a subsystem in the topological network, asshown at block 1310. The server 20 may obtain time series of measurementdata, such as flow-rate, pressure, compressor operation status, weatherinformation, and the like from the SCADA system for the stations in thesubsystem, as shown at block 1320. For example, the server 20 accessesthe historical temporal sensor measurements from the data repository 30.The historical temporal sensor measurements for a predeterminedtime-span may be accessed, for example, last six months, last one year,last 30 days, or any other predetermined time-span. In an example, thepredetermined time-span is selected such that the pipeline network 10has experienced one or more leaks due to ruptures of the infrastructurewithin the selected time-span. The server 20 may segregate the temporalsensor measurements, for example into a first subset that includessensor measurements at timestamps at which leak events occurred and asecond subset that includes sensor measurements at timestamps withoutleak events, as shown at blocks 1324 and 1326. In an example, the server20 identifies the temporal lags between the temporal sensor measurementsof the stations of the subsystem, as shown at block 1322. In an example,the temporal lags between the sensor measurements at the timestampswithout the leak events are determined. For example, the server 20 maycompute the temporal lags between two stations by finding the maxcorrelation between the two time series values of the temporal sensormeasurements at the two stations.

The server 20 generates a prediction model for each of the stations inthe subsystem based on the historical temporal sensor measurements. Theprediction model for each station may be unique. In another example, theserver 20 generates the prediction models for each station in parallel.For example, the server 20 generates a prediction model for a firststation in the subsystem, as shown at block 1330. The prediction modelpredicts sensor measurements at the first station based on the sensormeasurements from each station in the subsystem, the sensor measurementscorresponding to timestamps without leak events. Thus, the predictionmodel determines a relationship between the temporal sensor measurementsof the stations in the subsystem, during operation of the subsystemwithout a leak event. For example, if the sensor measurement predictedis pressure, the prediction model determines a multivariaterelationship, which may be expressed as in Table 4.

TABLE 4 P₀(t) ~ 

 (f₀(t), . . . , f_(n) ^(t,Lagged), P₁ ^(t,Lagged), . . . , P_(n)^(t,Lagged), T(t), C₀ ^(t,Lagged), . . . , C_(n) ^(t,Lagged)) where: nis the number of stations, and measurement points in the subsystem.P₀(t) is the control variable, i.e., pressure measurements in thesubsystem, f₀ ^(t,Lagged), . . . , f_(n) ^(t,Lagged) are theupstream/downstream flows in the pipe segment, and its lagged timeseries, f_(i) ^(t,Lagged) = [f_(i)(t − L), . . . , f_(i)(t − 1)] f₀(t)is the flow measurement from the same station as P₀ P₁ ^(t,Lagged), . .. , P_(n) ^(t,Lagged) are the pressure measurements at up-stream anddown-stream of the pipe segment, and its lagged time series. T(t) is theair temperature C₀ ^(t,Lagged), . . . , C_(n) ^(t,Lagged) are thecompressor RPMs, and its lagged time series.

The relationship F, may be a non-linear model that maps a set ofcontinuous variables to a controlled variable. For example, therelationship F may be determined based on robust machine learningtechniques such as gradient boost machine, support vector machine, lassolinear model, or any other machine learning techniques or a combinationthereof. Of course, in other examples the relationship may be determineddifferently, such as described elsewhere in this document. Accordingly,the server 20 determines a causality dependency of a sensor measurementat the first station and sensor measurements at other stations in thesubsystem, as well as other sensor measurements at the first station.For example, the server 20 determines a causality dependency betweenpressure measurement at the first station and a combination of flow-ratemeasurements at the first station, and pressure and flow-ratemeasurements at the other stations in the subsystem.

According to the relationship model F, given a set of sensormeasurements from the other stations in the subsystem and/or flow-ratemeasurement from the first station, the server 20 determines a predictedpressure measurement of the first station, as shown at block 1340. Thepredicted sensor measurement in this manner, such as the pressuremeasurement, is a benchmark value according to the relationship F. Therelationship may also be referred to as a prediction model as itpredicts the value of the sensor measurement given a set of inputs.

The server 20 computes a cumulative shift of the sensor measurement fromthe benchmark value by computing a cumulative sum of difference for thesensor measurement at the first station, as shown at block 1350. Forexample, as the sensor measurements are observed from the first station,a difference between each measurement and the corresponding benchmarkvalue is calculated, and the difference is cumulatively summed up, asshown at blocks 1352 and 1354. The server 20 further determines if the,sensor measurement does not deviate significantly from the benchmarkvalue by checking if the cumulative sum value is within a predeterminedrange, as shown at block 1360. Since measurements greater than thebenchmark and those less than the benchmark average each other out, thecumulative sum value varies narrowly around the benchmark value, andthus are within the predetermined range, in case there are no leaks. Ifthere is a leak, the sensor measurement is on one side of the benchmark,causing the cumulative sum value to depart progressively from that ofthe benchmark value, outside the predetermined range. Accordingly, theserver issues a leak detect notification in case the cumulative sumvalue is outside the predetermined range, as shown at block 1365.

Thus, the technical solutions based on the cumulative sum, facilitatecontinuously monitoring the pipeline network 10 for small anomalies,which may be caused by small damage on the pipeline, which may lead toextended small leaks in the system, or ruptures in the pipeline network10.

In addition, the technical solutions facilitate determining thepredetermined range that may be used to check if the cumulative sumvalue indicates a leak. The server 20 accesses the historical temporalsensor measurements of the first station as shown at block 1410. Theserver 20 further computes benchmark sensor measurements for the firststation based on the historical sensor measurements according to therelationship determined, as shown at block 1412. The server 20 furtheridentifies and selects the computed benchmark values corresponding tothe leak events, as shown at block 1414. The server 20 may furtheridentify actual sensor measurements at the timestamps of the leaks forthe first station, as shown at block 1414. The server 20 computescumulative sums of the differences between the benchmark values and theactual values of the sensor measurements, as shown at block 1416. Theserver 20 identifies the cumulative sums of differences at thetimestamps at which leaks occurred as threshold values to predict leaks,as shown at block 1420. In another example, the server 20 computes amean, or a standard deviation of the cumulative sums of differences atthe timestamps at which leaks occurred, as the thresholds, or boundariesof the predetermined range to check for prediction of leaks in thepipeline network 10.

Thus, the technical solutions described herein include detecting a smallleak in a subsystem, and in turn in a pipeline network, such as ahigh-pressure gas pipeline network. Detecting a small leak may preventruptures and other larger issues with the pipeline network.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, configuration data for integrated circuitry, oreither source code or object code written in any combination of one ormore programming languages, including an object oriented programminglanguage such as Smalltalk, C++, or the like, and procedural programminglanguages, such as the “C” programming language or similar programminglanguages. The computer readable program instructions may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider). In some embodiments, electronic circuitry including,for example, programmable logic circuitry, field-programmable gatearrays (FPGA), or programmable logic arrays (PLA) may execute thecomputer readable program instructions by utilizing state information ofthe computer readable program instructions to personalize the electroniccircuitry, in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks may occur out of theorder noted in the Figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application, or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A computer implemented method for controllingflow of fluid by predicting linepack delays, the method comprising:receiving, by a processing unit of a Supervisory control and dataacquisition system, temporal sensor measurements of a firstfluid-delivery pipeline network, wherein the temporal sensormeasurements comprises a series of sensor measurements from eachrespective station from the stations of the first fluid-deliverypipeline network; determining, by the processing unit, a topologicalnetwork of the stations based on a causality graph that indicatestemporal dependence between the stations of the first fluid-deliverypipeline network, wherein the topological network identifies a temporaldelay between a pair of stations in the first fluid-delivery pipelinenetwork, wherein determining the topological network comprises:selecting a node of the causality graph, wherein the selected nodecorresponds to a supply station of the first fluid-delivery pipelinenetwork; determining a set of nodes linked to the selected node in thecausality graph; identifying, from the set of nodes, a first node thathas the least temporal lag among the set of nodes; and removing, fromthe set of nodes, a second node that is linked to the first node;generating, by the processing unit, a temporal delay prediction modelbased on the topological network associated with the firstfluid-delivery pipeline network; predicting, by the processing unit, thelinepack delays of a second fluid-delivery pipeline network based on thetemporal delay prediction model generated using the first fluid-deliverypipeline network, wherein the second fluid-delivery pipeline network isa non-monitored pipeline network; and compressing, by a compressorstation of the second fluid-delivery pipeline network, fluid beingtransported by the second fluid-delivery pipeline network based on thepredicted linepack delays to maintain at least a predetermined pressurein the second fluid-delivery pipeline network.
 2. The computerimplemented method of claim 1, wherein generating the temporal delayprediction model comprises: identifying values of predeterminedattributes of the pair of stations in the first fluid-delivery pipelinenetwork; and mapping the temporal delay and the values of predeterminedattributes associated with the pair of stations in the firstfluid-delivery pipeline network.
 3. The computer implemented method ofclaim 2, wherein the predetermined attributes comprise at least one of alength of a pipeline between the pair of stations, a diameter of thepipeline, an elevation difference of the pipeline between the pair ofstations.
 4. The computer implemented method of claim 1, whereingenerating the temporal delay prediction model comprises: computingvalues of predetermined measurements of the pair of stations in thefirst fluid-delivery pipeline network; and mapping the temporal delayand the values of predetermined measurements associated with the pair ofstations in the first fluid-delivery pipeline network.
 5. The computerimplemented method of claim 4, wherein the predetermined measurementscomprise an average operating pressure and an average operating flowrate.
 6. The computer implemented method of claim 1, wherein thecausality graph comprises a plurality of nodes and a plurality of links,wherein the nodes are representative of the stations, and a pair ofnodes is connected by a link in response to the pair of Stations beingtemporally dependent.
 7. The computer implemented method of claim 1,wherein the first fluid-delivery pipeline network is a first portion ofa pipeline network, and the second fluid-delivery pipeline network is asecond portion of said pipeline network, wherein the first portion ismonitored by a supervisory control and data acquisition system.
 8. Asystem for controlling flow of fluid by predicting linepack delays, thesystem comprising: a memory; and a processor configured to: receivetemporal sensor measurements of a first fluid delivery pipeline network,wherein the temporal sensor measurements comprises a series of sensormeasurements from each respective station from the stations of thefluid-delivery pipeline network; determine a topological network of thestations based on a causal graph of the first fluid-delivery pipelinenetwork, wherein the topological network identifies a temporal delaybetween a pair of stations in the first fluid-delivery pipeline network,wherein determining the topological network comprises: selecting a nodeof the causality graph, wherein the selected node corresponds to asupply station of the first fluid-delivery pipeline network; determininga set of nodes linked to the selected node in the causality graph;identifying, from the set of nodes, a first node that has the leasttemporal lag among the set of nodes; and removing, from the set ofnodes, a second node that is linked to the first node; generate atemporal delay prediction model based on the topological networkassociated with the first fluid-delivery pipeline network; predict thelinepack delays of a second fluid-delivery pipeline network based on thetemporal delay prediction model generated using the first fluid-deliverypipeline network, wherein the second fluid-delivery pipeline network isa non-monitored pipeline network; and compress, by a compressor stationof the second fluid delivery pipeline network, fluid being transportedby the second fluid-delivery pipeline network based on the predictedlinepack delays to maintain at least a predetermined pressure in thesecond fluid-delivery pipeline network.
 9. The system of claim 8,wherein generation of the temporal delay prediction model comprises:identification of values of predetermined attributes of the pair ofstations in the first fluid-delivery pipeline net work; anddetermination of a mapping of the temporal delay and the values ofpredetermined attributes associated with the pair of stations in thefirst fluid-delivery pipeline network.
 10. The system of claim 9,wherein the predetermined attributes comprise at least one of a lengthof a pipeline between the pair of stations, a diameter of the pipeline,an elevation difference of the pipeline between the pair of stations.11. The system of claim 8, wherein generation of the temporal delayprediction model comprises: computation of values of predeterminedmeasurements of the pair of stations in the first fluid-deliverypipeline network; and determination of a mapping of the temporal delayand the values of predetermined measurements associated with the pair ofstations in the first fluid-delivery pipeline network.
 12. The system ofclaim 11, wherein the predetermined measurements comprise an averageoperating pressure and an average operating flow rate.
 13. The system ofclaim 8, wherein the causal graph of the first fluid-delivery pipelinenetwork is based on the temporal sensor measurements, and wherein thecausal graph comprises a plurality of nodes and a plurality of links,wherein the nodes are representative of the stations, and a pair ofnodes is connected by a link in response to the pair of stations beingtemporally dependent.
 14. The system of claim 8, wherein the secondfluid delivery pipeline network has physical dimensions identical to thefirst fluid-delivery pipeline network, and the second fluid-deliverypipeline network transfers a different amount of fluid than the firstfluid-delivery pipeline network.
 15. A computer program product forfacilitating a supervisory control and data acquisition (SCADA) systemto control flow of fluid by predicting linepack delays, the computerprogram product comprising a computer readable storage medium, thecomputer readable storage medium comprising computer executableinstructions, wherein the computer readable storage medium comprisesinstructions to: receive temporal sensor measurements of a first fluiddelivery pipeline network, wherein the temporal sensor measurementscomprises a series of sensor measurements from each respective stationfrom the stations of the fluid-delivery pipeline network; determine atopological network of the stations based on a causality graph, whereinthe topological network identifies a temporal delay between a pair ofstations in the first fluid-delivery pipeline network, whereindetermining the topological network comprises: selecting a node of thecausality graph, wherein the selected node corresponds to a supplystation of the first fluid-delivery pipeline network; determining a setof nodes linked to the selected node in the causality graph;identifying, from the set of nodes, a first node that has the leasttemporal lag among the set of nodes; and removing, from the set ofnodes, a second node that is linked to the first node; generate atemporal delay prediction model based on the topological networkassociated with the first fluid delivery pipeline network; predict thelinepack delays of a second fluid-delivery pipeline network based on thetemporal delay prediction model generated using the first fluid-deliverypipe line network, wherein the second fluid-delivery pipe line networkis a non-monitored pipeline network; and compress, by a compressorstation of the second fluid delivery pipeline network, fluid beingtransported by the second fluid-delivery pipeline network based on thepredicted linepack delays to maintain at least a predetermined pressurein the second fluid-delivery pipeline network.
 16. The computer programproduct of claim 15, wherein generation of the temporal delay predictionmodel comprises: identification of values of predetermined attributes ofthe pair of stations in the first fluid-delivery pipeline network; anddetermination of a mapping of the temporal delay and the values ofpredetermined attributes associated with the pair of stations in thefirst fluid-delivery pipeline network.
 17. The computer program productof claim 16, wherein the predetermined attributes comprise at least oneof a length of a pipeline between the pair of stations, a diameter ofthe pipeline, an elevation difference of the pipeline between the pairof stations.
 18. The computer program product of claim 15, whereingeneration of the temporal delay prediction model comprises: computationof values of predetermined measurements of the pair of stations in thefirst fluid-delivery pipeline network; and determination of a mapping ofthe temporal delay and the values of predetermined measurementsassociated with the pair of stations in the first fluid-deliverypipeline network, wherein the predetermined measurements comprise anaverage operating pressure and an average operating flow rate.
 19. Thecomputer program product of claim 15, wherein the temporal sensormeasurements of the fluid-delivery pipeline network comprise a series ofsensor measurements from each respective station from the stations ofthe fluid delivery pipeline network.
 20. The computer program product ofclaim 15, wherein the second fluid-delivery pipeline network transfers afluid that is compressed at a compression ratio different than a fluidin the first fluid-delivery pipeline network.